Part II Knowing How to Assess Chapter 5 Minimizing Error p115 Review of Appl 644 – Measurement Theory – Reliability – Validity Assessment is broader term than Measurement – What does this mean? chapter 5 Minimizing Error1

Background Queletet (1835) Queletet – Established the normal distribution – Used by: Galton (measurement of genius) Binet et al. Munsterberg (employment testing) J. M. Cattell (perceptual and sensory tests) Over time measurement – Focus changed from reliability to validity chapter 5 Minimizing Error2

Background Measurement Adolphe Quetelet – (1835) – conception of the homme moyen (“average man”) as the central value about which measurements of a human trait are grouped according to the normal distribution. normal distribution – Physical and mental attributes are normally distributed – Errors of measurement are normally distributed – Foundation for psychological measurement chapter 5 Minimizing Error3

RELIABILITY CONCEPTS OF MEASUREMENT ERROR p117 Measurement Error and Error variance – Table 5.1 Reasons for differences in performance I Person characteristics –long term, permanent – Influence scores on all tests, e.g. language, skills II Person characteristics specific to test – E.g type of words on test more/less recognizable to some III temporary characteristics that – Influence scores on any test (e.g. evaluation apprehension) IV Temporary and specific to the test – E.g. stumped by a work, e.g. V Administration effects – E.g. interaction administrator and examinee VI pure chance chapter 5 Minimizing Error4

Category II A – of most interest – Others reflect unwanted sources of variance Classical theory: X = t + e Assumptions: (errors are truly random) – Obtained score = algebraic sum of t+e – Not correlated: t scores and e scores (in one test) errors in different measures errors in one measure with true scores in another chapter 5 Minimizing Error5

Measurement Error X = s + e (one individual’s score) – Why was t replaced with s? σ x 2 = σ s 2 + σ e 2 – total variance (all scores) = systematic causes + random error chapter 5 Minimizing Error6

Reliability chapter 5 Minimizing Error7

Reliability chapter 5 Minimizing Error8

Reliability and Validity chapter 5 Minimizing Error9

Accuracy/reliability/validity Accuracy is ≠ reliability – An inaccurate thermometer may be consistent (reliable) Accuracy is ≠ validity – An inaccurate thermometer may show validity (high correlations with Bureau of standards instrument – But is inaccurate (consistently lower for each paired observation), i.e. not accurate Why is the concept of “accuracy” meaningless for psychological constructs? chapter 5 Minimizing Error10

RELIABILITY ESTIMATION p125 Coefficients of Stability – Over time Coefficients of Equivalence – Equivalent forms (e.g. A and B) Coefficients of Internal Consistency – Kuder-Richardson Estimates (assumes homogeneity) K-R 20 (preferred) Cronbach’s alpha α (general version of K-R 20) Where is this in SPSS? chapter 5 Minimizing Error11

Reliability Estimation (con’t) Inter-rater Agreement v. reliability – ICC – Rwg – % agreement (Kappa) – See Rosenthal & Rosnow table (hand out) Comparisons Among Reliability Estimates – Systematic variance must be stable characteristics of examinee what is measured Use estimates that make sense for the purpose, – For re-testing what’s most appropriate? – For production over a long period? – An e.g. of a job requiring stability of attribute? chapter 5 Minimizing Error12

chapter 5 Minimizing Error13

Interpretations of Reliability Coefficients p133 Important to remember: – Size of coefficient needed depends upon: The purpose for which it is used The history of the type of measure – what would be acceptable for a GMA – for an interview? Length of test (how many items are needed?) chapter 5 Minimizing Error14

VALIDITY: AN EVOLVING CONCEPT p134 Why is it important for I/O to distinguish between – A Test “… purports to measure something” – validity “the degree it measures what it purports to” – Validity in “predicting to a criterion” (making inferences) Three Troublesome Adjectives – Content, criterion related, construct Meaning v. interpretation v. inferences about a person What’s troublesome and what’s more important? Descriptive and Relational Inferences – Descriptive inferences (about the score itself) High IQ means the person is smart (trait) – Relational inferences (about what can be predicted) High scorer will perform on the job (sign) chapter 5 Minimizing Error15

Psychometric Validity v. Job Relatedness Psychometric Validity – Confirm the meaning of the test intended by the test developer Examples? – Disconfirm plausible alternatives Examples? How dos psychometric validity differ from Job- relatedness chapter 5 Minimizing Error16

VARIETIES OF PSYCHOMETRIC VALIDITY EVIDENCE p137 Evidence Based on Test Development – Provide evidence for a test you plan to use – questions to guide evaluation: answer them for your job Did the developer have a clear idea of the attribute? Are the mechanics of the measurement consistent with the concepts? Is the stimulus content appropriate? What the test carefully and skillfully developed? Evidence Based on Reliability - questions to guide evaluation: answer them for your job Is the internal statistical evidence satisfactory? Are scores stable over time and consistent with alternative measures? chapter 5 Minimizing Error17

Evidence from Patterns of Correlates – Confirmatory and dis-confirmatory Questions for evaluation: – Answer them for a test you will use Does empirical evidence confirm logically expected relations with other variables? Does empirical evidence disconfirm alternative meanings of test scores? Are the consequences of the test consistent with the meaning of the construct being measured? chapter 5 Minimizing Error18

Beyond Classical Test Theory p144 Factor Analysis (identify latent variables in a set of scores) – EFA (Exploratory) – CFA (Confirmatory) – Which would be most likely to be used to develop a test? chapter 5 Minimizing Error19

GENERALIZABILITY THEORY Can the validity of the test be generalized to: – other times? – Other circumstances? – Other behavior samples? – Other test forms? – Other raters/ interviewers? – Other geographical populations? Give an example of where a test will not perform the same for applicants in different geographical locations chapter 5 Minimizing Error20

ITEM RESPONSE THEORY P148 Classical test: – A person’s score on a test relates to others IRT – A person’s score on a test reflects standing on the latent variable (i.e. “sample free”) Computerized adaptive testing with IRT Analysis of Bias with Adverse Impact – Differential item functioning chapter 5 Minimizing Error21